Writer identification using edge-based directional probability distribution features for arabic words

Generating accurate high-resolution images from text representations is a difficult problem in computer vision that has a wide range of functional applications. Text-to-image conversion is not dissimilar to the difficulties inherent in language processing. For example, each text meaning can be encoded in two distinct human languages, while photographs and text are two distinct encoding languages for similar data. However, these are two distinct issues, since text-to-image or image-to-text conversions are extremely multimodal in nature. The proposed model for creating 256 × 256 realistic images from Arabic text descriptions is discussed in this article. The relationship between an Arabic word in a sentence and its component in a picture as introduced in this paper using the DAMSM model. This model teaches two neural networks how to map the Arabic picture and word sub-regions of a full sentence to a shared semantic model. It performs well as an Arabic-text encoder and a picture encoder. We start with the Modified-Arabic dataset and train the model from scratch. The proposed model establishes a new standard for the conversion of Arabic text to realistic pictures. A mutation happens when Arabic is used as the primary language for converting Arabic texts to real images. The inception score of the newly introduced model reported by 3.42 ± .05 on the CUB database.

This paper presents the use of Hidden Markov Models (HMM) to classifying Arabic words that have been represented by moments into a set of predefined classes. The proposed scheme results in enhanced performance because it deals effectively with errors that commonly originate during the feature extraction stage and it accounts for variations due to the individual handwriting performance of different data sets. To overcome the variations in images, each is represented by rotation and scale invariant feature vector known as Pseudo Zernike Moments (PZM). To demonstrate its effectiveness, the proposed classification scheme has been employed in the context of different sets of handwritten Arabic words. The classification results demonstrate that the proposed approach can classify handwritten Arabic word with a error rate less than 10%.

Offline Arabic handwritten word recognition: A transfer learning approach

In this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually revised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the performance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.

Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking

Arabic handwritten text recognition has not received the same attention as that directed towards Latin script-based languages. In this paper, we present our efforts to develop a comprehensive Arabic Handwritten Text database (AHTD). At this stage, the database will consist of text written by 1000 writers from different countries. Currently, it has data from over 300 writers. It is composed of an images database containing images of the written text at various resolutions, and a ground truth database that contains meta-data describing the written text at the page, paragraph, and line levels. Tools to extract paragraphs from pages, segment paragraphs into lines have also been developed. Segmentation of lines into words will follow. The database will be made freely available to researchers world-wide. It is hoped that the AHTD database will stir research efforts in various handwritten-related problems such as text recognition, and writer identification and verification.

Arabic handwritten text recognition is a complex and challenging research domain. This study proposes an offline Arabic handwritten word recognition system based on transfer learning. The system exploits four pre-trained convolutional neural network (CNN) architectures, namely VGG16, ResNet50, AlexNet, and InceptionV3. In addition, a specialized image recognition model derived from the ImageNet dataset is incorporated. A combination strategy is designed to combine transfer learning with specific fine-tuning techniques, aiming to improve recognition accuracy. The study is conducted on the IFN/ENIT dataset, which includes images of Tunisian City and village names. The results show that the proposed system achieves a recognition accuracy of 94.73%, which is significantly higher than the accuracy rates achieved by previous approaches. These results suggest that the proposed system is a promising approach for Arabic handwritten text recognition.

Handwritten text in any language is believed to convey a great deal of information about writers' personality and identity. Handwritten documents are frequently used as evidences in forensic tasks. Handwriting skills is learnt and developed from the early schooling stages. Research interest in behavioral biometrics was the main driving force behind the growth in research into Writer Identification (WI) from handwritten text, but recent rise in terrorism associated with extreme religious ideologies spreading primarily, but not exclusively, from the middle-east has led to a surge of interest in WI from handwritten text in Arabic and similar languages. This paper is the main outcome of extensive research investigations conducted with the aim of developing an automatic identification of a person from handwritten Arabic text samples. For the dependent 8-dimensional WI scheme, we identify the best performing set of subwords (best 22 subwords out of 49 then followed by best 11 out of these 22 subwords). We established the validity of our hypothesis for different versions of subwords based Writer identification WI schemes by providing empirical evidence when testing on a number of existing text-dependent. The database consisted of 20 text samples from 95 writers. In these experiments we used two different similarity functions, one where we considered the Euclidian distance function while the other one combines the Dynamic Time Warping (DTW) for the projection attributes with Euclidian for the other features. The experimental results confirmed beyond any doubt the validity of our hypothesis and outperformed existing word-base WI, which only achieves high accuracy at rank 10 nearest neighbours. The 11 subwords based schemes with the DTW related similarity function achieved the higher accuracy in top rank around 98.12% accuracy rate of WI.

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.

<i>Aaru</i>

&lt;span lang="EN-US"&gt;Arabic handwritten text recognition has long been a difficult subject, owing to the similarity of its characters and the wide range of writing styles. However, due to the intricacy of Arabic handwriting morphology, solving the challenge of cursive handwriting recognition remains difficult. In this paper, we propose a new efficient based image processing approach that combines three image descriptors for the feature extraction phase. To prepare the training and testing datasets, we applied a series of preprocessing techniques to 100 classes selected from the handwritten Arabic database of the &lt;/span&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;Institut Für Nachrichtentechnik/Ecole Nationale d'Ingénieurs de Tunis&lt;/span&gt;&lt;/em&gt;&lt;span lang="EN-US"&gt; (IFN/ENIT). Then, we trained the k-nearest neighbor’s algorithm (k-NN) algorithm to generate the best model for each feature extraction descriptor. The best k-NN model, according to common performance evaluation metrics, is used to classify Arabic handwritten images according to their classes. Based on the performance evaluation results of the three k-NN generated models, the majority-voting algorithm is used to combine the prediction results. A high recognition rate of up to 99.88% is achieved, far exceeding the state-of-the-art results using the IFN/ENIT dataset. The obtained results highlight the reliability of the proposed system for the recognition of handwritten Arabic words.&lt;/span&gt;

Steganography is a method of hiding data within a cover media so that other individuals fail to realize their existence. In this paper, a new approach for steganography in Arabic texts is proposed. The main idea is that each Arabic word may have some characters which can be extended by dasiaKashidapsila. The ranks dasialocationspsila of such characters and the inserted Kashida, construct a coding method to represent a block of secret bits. Different scenarios have been proposed based on the maximum number of Kashida possible to be inserted per word. The approach was compared to some existing Arabic text steganography approaches in terms of capacity and security. It is shown that this proposed approach outperforms the others with interesting promising results.

Although in the digital humanities, researchers use software tools to conduct their research, and often apply these tools to data the software was not developed for, there has been little attention for investigating tool performance on this data. This is strange because in order to be able to appraise the results of digital humanities research, it is important to understand to what extent the tool output is correct. To illustrate the importance of the validation of tools, this article presents a case study of validating Arabic root extraction tools. Arabic words are based on root letters; three root letters usually demarcate a semantic field. Thus, roots can be used for studying semantic fields. For example, researchers can gain insight into the relative importance of the different senses (i.e. seeing, hearing, touching, smelling, and tasting) in Arabic jurisprudence (fiqh) by extracting and counting roots. A problem is that there are only a few usable tools available. We take three root extraction tools, Khoja (Khoja and Garside, 1999, Stemming Arabic Text. Lancaster, England: Lancaster University), ISRI (Taghva et al., 2005, Arabic stemming without a root dictionary. In International Conference on Information Technology: Coding and Computing (ITCC’05). Vol. 2. Las Vegas, NV, April 2005 pp. 152–57), and AlKhalil (Boudlal et al., 2010, Alkhalil morpho sys1: a morphosyntactic analysis system for Arabic texts. In International Arab Conference on Information Technology. New York, NY: Elsevier Science Inc., April 2017, pp. 1–6), and create manually annotated gold standard data consisting of three samples of approximately 1,000 words from important books of Islamic jurisprudence. We show that Khoja is the best root extraction tool for our data. We also demonstrate that the relative counts of individual roots differ among tools, which leads to a different interpretation depending on which tool is chosen. This means that findings based on automatically extracted roots should always be interpreted with care.

In this paper, we present the first truly 2-D recognition system of Arabic handwritten words. The used recognizer is the NSHPZ-HMM model with a new zoning design approach. The zoning method we propose depends on the baseline of the treated word. The key point relies in the ability of our baseline to perfectly follow the writing curve even in case of vertical ligatures and of different slant angles within the same Arabic word. The result is an appropriate division in zones of an image word that facilitates its modelization through the use of the NSHPZ-HMM model. Experiments on Arabic literal amounts of AHDB database show the efficiency of the proposed recognition system over an NSHPZ-HMM using zoning independent baseline, a classical pixel-based NSHP-HMM and a set of state of the art systems developed on the same database.